import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
     import onnxruntime as ort
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						Union[str, Any]=0				):





    """simple docstring"""


    UpperCamelCase			  =      floats_tensor((1, 3, 128, 128)   ,   rng=random.Random(lowerCamelCase_				)				)
    UpperCamelCase			  =      np.random.RandomState(lowerCamelCase_				)
    UpperCamelCase			  =      {
        """prompt""": """A painting of a squirrel eating a burger""",
        """image""": image,
        """generator""": generator,
        """num_inference_steps""": 3,
        """strength""": 0.7_5,
        """guidance_scale""": 7.5,
        """output_type""": """numpy""",
    }
    return inputs







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,   provider="""CPUExecutionProvider"""				)
    pipe.set_progress_bar_config(disable=lowerCamelCase_				)

    UpperCamelCase			  =      self.get_dummy_inputs()
    UpperCamelCase			  =      pipe(**lowerCamelCase_				).images
    UpperCamelCase			  =      image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 128, 128, 3)
    UpperCamelCase			  =      np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]				)
    assert np.abs(image_slice - expected_slice				).max() < 1E-1







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,   provider="""CPUExecutionProvider"""				)
    UpperCamelCase			  =      PNDMScheduler.from_config(pipe.scheduler.config   ,   skip_prk_steps=lowerCamelCase_				)
    pipe.set_progress_bar_config(disable=lowerCamelCase_				)

    UpperCamelCase			  =      self.get_dummy_inputs()
    UpperCamelCase			  =      pipe(**lowerCamelCase_				).images
    UpperCamelCase			  =      image[0, -3:, -3:, -1]

    assert image.shape == (1, 128, 128, 3)
    UpperCamelCase			  =      np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,   provider="""CPUExecutionProvider"""				)
    UpperCamelCase			  =      LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
    pipe.set_progress_bar_config(disable=lowerCamelCase_				)

    # warmup pass to apply optimizations
    UpperCamelCase			  =      pipe(**self.get_dummy_inputs()				)

    UpperCamelCase			  =      self.get_dummy_inputs()
    UpperCamelCase			  =      pipe(**lowerCamelCase_				).images
    UpperCamelCase			  =      image[0, -3:, -3:, -1]

    assert image.shape == (1, 128, 128, 3)
    UpperCamelCase			  =      np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,   provider="""CPUExecutionProvider"""				)
    UpperCamelCase			  =      EulerDiscreteScheduler.from_config(pipe.scheduler.config				)
    pipe.set_progress_bar_config(disable=lowerCamelCase_				)

    UpperCamelCase			  =      self.get_dummy_inputs()
    UpperCamelCase			  =      pipe(**lowerCamelCase_				).images
    UpperCamelCase			  =      image[0, -3:, -3:, -1]

    assert image.shape == (1, 128, 128, 3)
    UpperCamelCase			  =      np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase			  =      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,   provider="""CPUExecutionProvider"""				)
    UpperCamelCase			  =      EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config				)
    pipe.set_progress_bar_config(disable=lowerCamelCase_				)

    UpperCamelCase			  =      self.get_dummy_inputs()
    UpperCamelCase			  =      pipe(**lowerCamelCase_				).images
    UpperCamelCase			  =      image[0, -3:, -3:, -1]

    assert image.shape == (1, 128, 128, 3)
    UpperCamelCase			  =      np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint   ,   provider="""CPUExecutionProvider"""				)
    UpperCamelCase			  =      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
    pipe.set_progress_bar_config(disable=lowerCamelCase_				)

    UpperCamelCase			  =      self.get_dummy_inputs()
    UpperCamelCase			  =      pipe(**lowerCamelCase_				).images
    UpperCamelCase			  =      image[0, -3:, -3:, -1]

    assert image.shape == (1, 128, 128, 3)
    UpperCamelCase			  =      np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]				)

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-1





@nightly
@require_onnxruntime
@require_torch_gpu
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   @property
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    return (
        "CUDAExecutionProvider",
        {
            "gpu_mem_limit": "15000000000",  # 15GB
            "arena_extend_strategy": "kSameAsRequested",
        },
    )







   @property
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      ort.SessionOptions()
    UpperCamelCase			  =      False
    return options







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/img2img/sketch-mountains-input.jpg"""				)
    UpperCamelCase			  =      init_image.resize((768, 512)				)
    # using the PNDM scheduler by default
    UpperCamelCase			  =      OnnxStableDiffusionImgaImgPipeline.from_pretrained(
        """CompVis/stable-diffusion-v1-4"""   ,   revision="""onnx"""   ,   safety_checker=lowerCamelCase_   ,   feature_extractor=lowerCamelCase_   ,   provider=self.gpu_provider   ,   sess_options=self.gpu_options   ,   )
    pipe.set_progress_bar_config(disable=lowerCamelCase_				)

    UpperCamelCase			  =      """A fantasy landscape, trending on artstation"""

    UpperCamelCase			  =      np.random.RandomState(0				)
    UpperCamelCase			  =      pipe(
        prompt=lowerCamelCase_   ,   image=lowerCamelCase_   ,   strength=0.7_5   ,   guidance_scale=7.5   ,   num_inference_steps=10   ,   generator=lowerCamelCase_   ,   output_type="""np"""   ,   )
    UpperCamelCase			  =      output.images
    UpperCamelCase			  =      images[0, 255:258, 383:386, -1]

    assert images.shape == (1, 512, 768, 3)
    UpperCamelCase			  =      np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]				)
    # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 2E-2







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/img2img/sketch-mountains-input.jpg"""				)
    UpperCamelCase			  =      init_image.resize((768, 512)				)
    UpperCamelCase			  =      LMSDiscreteScheduler.from_pretrained(
        """runwayml/stable-diffusion-v1-5"""   ,   subfolder="""scheduler"""   ,   revision="""onnx"""				)
    UpperCamelCase			  =      OnnxStableDiffusionImgaImgPipeline.from_pretrained(
        """runwayml/stable-diffusion-v1-5"""   ,   revision="""onnx"""   ,   scheduler=lowerCamelCase_   ,   safety_checker=lowerCamelCase_   ,   feature_extractor=lowerCamelCase_   ,   provider=self.gpu_provider   ,   sess_options=self.gpu_options   ,   )
    pipe.set_progress_bar_config(disable=lowerCamelCase_				)

    UpperCamelCase			  =      """A fantasy landscape, trending on artstation"""

    UpperCamelCase			  =      np.random.RandomState(0				)
    UpperCamelCase			  =      pipe(
        prompt=lowerCamelCase_   ,   image=lowerCamelCase_   ,   strength=0.7_5   ,   guidance_scale=7.5   ,   num_inference_steps=20   ,   generator=lowerCamelCase_   ,   output_type="""np"""   ,   )
    UpperCamelCase			  =      output.images
    UpperCamelCase			  =      images[0, 255:258, 383:386, -1]

    assert images.shape == (1, 512, 768, 3)
    UpperCamelCase			  =      np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]				)
    # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

    assert np.abs(image_slice.flatten() - expected_slice				).max() < 2E-2

from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      input("""Enter image url: """).strip()
     print(F'''Downloading image from {url} ...''')
     _SCREAMING_SNAKE_CASE 		=      BeautifulSoup(requests.get(url).content, """html.parser""")
     # The image URL is in the content field of the first meta tag with property og:image
     _SCREAMING_SNAKE_CASE 		=      soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
     _SCREAMING_SNAKE_CASE 		=      requests.get(image_url).content
     _SCREAMING_SNAKE_CASE 		=      F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
     with open(file_name, """wb""") as fp:
          fp.write(image_data)
     print(F'''Done. Image saved to disk as {file_name}.''')
